The Best LLMs in 2026: A Plain-English Comparison

Two years ago, picking an AI model meant choosing between a handful of names. Today there are dozens, a new one seems to land every other Tuesday, and the launch-day hype around each is loud enough to drown out the part you actually care about: which one should you use?

This is a plain-English guide to the large language models (LLMs) that matter in 2026 - the engines behind ChatGPT, Claude, Gemini, and a wave of powerful open models from teams like DeepSeek, Qwen, and Kimi. No computer science degree required. We’ll compare them the way a busy person actually decides: what’s it good at, what does it cost, and what’s the catch.

A quick word on where we’re standing. We’re MindsHub, by MindsDB - we build the platform that open-source AI agents run on, and those agents call every one of these models, all day, to get real work done. Keeping score on which model is best for which job isn’t a hobby for us; it’s the job. So this comparison comes from running these models in production, not just reading their announcement posts.

The short version, if you’re in a hurry

You don’t need to memorize a leaderboard. For most people, the decision collapses to four cases:

• Best all-rounder for daily work - GPT-5.6 (OpenAI) or Claude Sonnet 5 (Anthropic). Either will draft, summarize, analyze, and code well enough that you’ll rarely hit a wall.
• Best for the genuinely hard stuff - Claude Opus 5, released July 24, 2026, which took first place on the public intelligence rankings on its launch day, or GPT-5.6 Sol (the flagship tier of OpenAI’s new Sol/Terra/Luna lineup). Claude Fable 5 is still Anthropic’s top capability tier, at twice Opus 5’s price. When the task is a tangled analysis, a long document, or work an AI has to carry across many steps, these hold their train of thought the longest. Grok 4.6, new this week, now ties GPT-5.6 Sol on the same rankings for a fifth of its output price - the value pick at the frontier, as long as your work isn’t terminal-heavy.
• Best for huge documents, images, audio, or video - Gemini 3.1 Pro (Google). It can read a 900-page PDF or an hour of video in one go. Qwen3.8-Max, Alibaba’s new flagship, is the value alternative: it ranks second on the public Vision Arena at half Gemini’s output price.
• Best bang for the buck - open models like GLM-5.2 and DeepSeek V4 Flash. They deliver most of the frontier’s quality at a small fraction of the price, and because the weights are published you can rent them from whichever host is cheapest, or run them on your own hardware. That choice is about to earn its keep: DeepSeek’s own API rates rise steeply on August 17, 2026, and third-party hosts aren’t following.

The rest of this guide explains the why behind those picks - and why the smartest teams have quietly stopped picking just one.

The 2026 LLM comparison table

Here’s the landscape at a glance, grouped by maker, in rough order of overall adoption today - a blend of everyday usage and professional traction, not a quality ranking or our preference. There’s no single “best” model, so don’t read the top row as a winner or the bottom as a loser: let the Best for column and the price guide you, not a model’s position. The Cost column gives a rough tier - $ (budget or open) to $$$$ (frontier) - next to the list API price per million tokens (input / output). Context is how much a model can read at once; 1M tokens is roughly 750,000 words, or a long book.

*Qwen3.8-Max’s weights are due the week of August 10, 2026 - it’s API-only until they land. Alibaba also ships a prolific open-weight Qwen line you can download and self-host at rock-bottom prices (~$0.40 / $1.20 through hosts, 256K context natively). †Kimi K3’s weights are published, but under Moonshot’s own license rather than MIT or Apache: fine for self-hosting, worth a read if you plan to resell access. ‡Open-weight models have no single price - you pay whichever host you rent from, or nothing but your own hardware. We quote Fireworks serverless list rates for DeepSeek, GLM and Kimi so the open rows compare against one host on one card; Qwen3.8-Max is Alibaba’s own API, since its weights aren’t out. Renting DeepSeek from DeepSeek itself is cheaper today and gets sharply more expensive on August 17, 2026 - see the DeepSeek section. All other prices are provider list rates as of August 13, 2026, and consumer apps like ChatGPT, Claude, and Gemini charge a flat monthly fee instead of per token. Gemini 3.1 Pro and Grok 4.6 are tiered - the higher figure kicks in on prompts over ~200K tokens (GPT-5.6 similarly costs more above ~270K). Claude Sonnet 5’s $2 / $10 was an introductory rate until August 11, 2026, when Anthropic made it permanent. Always check the provider for the current number.

By the numbers. Artificial Analysis rolls dozens of benchmarks into a single Intelligence Index. As of August 13, 2026, Claude Opus 5 holds first place - one point clear of Claude Fable 5, at half Fable’s price. Behind them sits a two-way tie about 3% off the lead: GPT-5.6 Sol and, as of this week, Grok 4.6. Moonshot’s Kimi K3 is about 5% back and Alibaba’s Qwen3.8-Max about 8% back, both ahead of Claude Opus 4.8. Meta’s Muse Spark 1.2 and Claude Sonnet 5 land roughly 10-13% off. GLM-5.2 and DeepSeek’s newest V4 Pro build sit about 16% off the lead, with DeepSeek V4 Flash a point behind them while costing pennies on the dollar. The order reshuffles almost weekly, so treat it as a snapshot - and because the field is packed this tightly at the top, cost and speed usually matter more than the top-line score.

How to read benchmarks without getting fooled

Benchmark scores are useful, but they’re a starting point, not a verdict. A few things worth knowing before you let a leaderboard make your decision:

• The test isn’t your job. A model that aces graduate-level physics questions might still write clunky marketing emails. The benchmark that matters most is your own work - try two or three models on a real task you do every week.
• Scores leak. Popular test questions sometimes end up in the training data, so a model can look smarter than it is simply because it has seen the answer key.
• “Smartest” rarely means “best for you.” The top model is also usually the slowest and priciest. For a lot of everyday work, a cheaper, faster model is indistinguishable in quality - and far nicer to your budget.
• The price column below is a unit price, not a bill. Qwen3.8-Max costs five times less per token than GPT-5.6 Sol and lands within ten cents of it on a real task, because it spends far more tokens getting there. We measured that gap across every model in this table in why cheap models cost more per task.
• One model, several scores. Leaderboards now list a model once per reasoning effort setting - how long it’s allowed to think before answering. Claude Opus 5 appears at five settings spanning eleven points, top to bottom. Headline numbers are almost always the most expensive setting, which is rarely the one you’d run all day.

If you want a deeper checklist for production use, we wrote a companion piece on the 12 things to weigh when choosing an LLM. For everyone else, the rundown below is enough.

The models, one by one

OpenAI - GPT-5.6 (Sol, Terra, and Luna)

GPT is the name most people will recognize, because it’s what powers ChatGPT. In July 2026 OpenAI shipped its new flagship generation, GPT-5.6, and with it a new naming scheme: the number is the generation, and three tier names - Sol, Terra, Luna - replace the old “mini / nano” convention:

• GPT-5.6 Sol - the flagship. The strongest all-rounder OpenAI has shipped: it sits about 3% off the top of the public intelligence rankings, and it shares first place on the public coding-agent rankings with Claude Opus 5. Frontier pricing: $5 / $30 per million tokens (a token is a chunk of text, about ¾ of a word).
• GPT-5.6 Terra - the balanced mid-tier ($2 / $12). Most of Sol’s ability at well under half the price; the sensible default for everyday work.
• GPT-5.6 Luna - the fast, cheap tier, and now startlingly cheap at $0.20 / $1.20. For high-volume, simpler work - tagging, summarizing, extraction - it ranks alongside the best open models while costing less to rent than most of them cost to host.

Both of those are new numbers: on July 30, 2026, three weeks after launch, OpenAI cut Luna’s price by 80% and Terra’s by 20%, crediting efficiency gains in how it serves them. Sol held at its launch rate. It’s the clearest sign yet that the competition has moved from who scores highest to who can serve it cheapest - good news if you’re buying.

Worth knowing: GPT-5.6 had one of the stranger launches of the year - a two-week, government-coordinated preview limited to about twenty organizations before general availability opened on July 9, 2026 (a recurring theme this summer; see the Fable 5 story below). The previous generation hasn’t vanished, either: GPT-5.5 still handles everyday ChatGPT replies and remains available in the API, alongside the Codex coding specialists and a Pro tier ($30 / $180) that throws parallel reasoning at the very hardest questions.

Anthropic - Claude (Opus 5, Fable 5, and Sonnet 5)

Claude is the model of choice when reasoning and reliability matter most. Anthropic has been shipping at a punishing pace - five releases since late May - and as of today there are three names worth knowing:

• Claude Opus 5 - released July 24, 2026, and still the leader of the public intelligence rankings. It’s built for agentic coding and long-horizon work: Anthropic reports 96% on SWE-bench Verified (a standard test of fixing real bugs in real open-source repositories), and it now shares the top of the public coding-agent rankings with GPT-5.6 Sol. It costs $5 / $25 per million tokens - the same as the Opus it supersedes, and half of Fable 5 - and it’s already the default model in Anthropic’s own coding tools. One caveat worth knowing before you trust it on facts: it scores higher on knowledge than the model it replaces, but it also invents more, so verify factual claims.
• Claude Fable 5 - Anthropic’s top capability tier, built for the hardest reasoning and for agents that run for days at a stretch. Its own documentation still calls it Anthropic’s most capable model, even though Opus 5 now edges it on the composite rankings - a distinction worth holding lightly, since the two are one point apart. Premium pricing to match: $10 / $50 per million tokens - reach for it when the task genuinely justifies it.
• Claude Sonnet 5 - the sweet spot, released June 30, 2026, and still the default model in the Claude apps for most subscribers. Anthropic calls it its most agentic Sonnet yet - on some tool-driving benchmarks it actually edges out Opus. It has also quietly become a third cheaper: the $2 / $10 introductory rate was due to expire at the end of August, and on August 11, 2026, Anthropic made it permanent, against the $3 / $15 the old Sonnet charged. It still works in a chattier, more thorough style, so real per-task costs can land higher than the sticker suggests - though that price cut closes most of the gap.

The Opus that Opus 5 replaces, Claude Opus 4.8, hasn’t gone anywhere - same $5 / $25 price, still supported, and no retirement date announced. It has also picked up a second job: Anthropic’s newest models run security classifiers that sometimes decline security-adjacent work, and when Opus 5 declines a request on those grounds, it’s automatically re-run on Opus 4.8.

Claude has a reputation for writing that sounds less robotic and for being careful about getting things right, which is why it’s a favorite for legal, financial, and other detail-heavy work. It’s also become the lab to beat in business - Anthropic has passed OpenAI in revenue, wins most head-to-head enterprise deals, and powers many of the most popular AI coding tools - which is why Claude ranks where it does here, despite a smaller consumer audience than ChatGPT or Gemini.

Fable 5 also carries one of the stranger stories in AI this year. Days after it first shipped in June 2026, the US government pulled it off the market under an emergency export-control order, citing its ability to find and exploit software vulnerabilities. Anthropic added new safeguards, the order was lifted on June 30, and Fable 5 came back - globally - on July 1, 2026. It’s the first time a widely deployed AI model has been suspended and reinstated by government order, and a useful reminder of how fast this landscape moves.

Google - Gemini (3.1 Pro and 3.6 Flash)

Gemini’s superpower is breadth. It’s natively multimodal, which is a technical way of saying it reads text, images, audio, and video equally well - and it can take in an absurd amount at once. Hand Gemini 3.1 Pro a 900-page PDF, a year’s worth of meeting recordings, or a long video, and it’ll work through the whole thing in a single pass. If your work involves wrangling big, messy, mixed documents, it’s hard to beat. One budgeting note: for prompts over ~200K tokens, Gemini 3.1 Pro’s rate climbs to about $4 / $18 per million in / out, and the giant-document jobs it’s best at are exactly the ones that cross that line - so price them at the upper tier.

Gemini 3.6 Flash, released July 21, 2026, is the lighter, much faster sibling - it matches the 3.5 Flash it succeeds on smarts while spending about 17% fewer output tokens per task, and it trims the output price from $9 to $7.50 per million (input holds at $1.50). Google’s headline gains are in agentic coding and computer use, and it remains a strong default for high-volume, everyday work. Two siblings landed the same day: Gemini 3.5 Flash-Lite at $0.30 / $2.50 for the truly high-throughput stuff, and a security-focused Gemini 3.5 Flash Cyber in limited pilot. Gemini also has the obvious home-field advantage if you live in Google Workspace. The heavier Gemini 3.5 Pro, announced back in May 2026, keeps slipping - Google says it’s still testing with partners - so don’t plan around it just yet; in the meantime, Google has teased that training is already underway on Gemini 4.

Meta - Muse

Meta spent years as the champion of open-weight AI with Llama - and in 2026 it changed course. The new Muse family, from Meta Superintelligence Labs, is closed: no downloadable weights, no open license, just an API and Meta’s own apps. The current release, Muse Spark 1.2 (August 5, 2026), is a capable agent-focused model that scores a shade above GLM-5.2, reads a million tokens, and is priced aggressively at $1.25 / $4.25 per million tokens. Watch one unusual line on its price sheet: a “contributor” tier at $0.10 / $0.20 - roughly a fifteenth of standard - in exchange for letting Meta train future models on your prompts and the model’s replies. That’s a fair trade for hobby projects and an easy no for anything confidential. It also powers the free Meta AI assistant across WhatsApp, Instagram, Facebook, and Messenger - which arguably makes it the most widely deployed model on this list, even if it’s not the one people name. Meta says larger Muse models are in training, and that it “hopes” to open-source future versions. If you’re here because you liked Llama’s openness, that spirit now lives with GLM, DeepSeek, and Qwen.

DeepSeek - V4

DeepSeek is the model that rattled the industry by proving you don’t need a frontier-sized budget to get near-frontier results. DeepSeek V4 is open-weight - published under a permissive MIT license, so anyone can download it, inspect it, and run it on their own machines. It came out of preview on July 20, 2026, in two flavors: the full-strength V4 Pro and the cheaper, faster V4 Flash. Both are in our table, and for several weeks the ordering was genuinely odd: Flash shipped later and outscored Pro on the public intelligence index while costing about a third as much. DeepSeek closed that gap on August 12, 2026, with a new V4 Pro build that puts the flagship back on top by a point. Flash still wins on price by roughly three to one, so it stays the sensible default for high-volume work; reach for Pro when a task needs the extra headroom. Either way you get a million-token context and a bill that rounds to nothing: renting Flash from Fireworks, its output runs at $0.28 per million tokens, roughly a hundredth of the closed flagships’ rate.

Where you rent it from is about to matter more than it ever has. The prices in our table are Fireworks’ - a host that runs the published weights on its own hardware. You can also buy DeepSeek from DeepSeek, and until now that was the cheaper door: V4 Pro at $0.44 / $0.87 against Fireworks’ $1.74 / $3.48, with Flash priced identically at both.

On August 17, 2026, DeepSeek’s own API gets sharply more expensive. From midnight Beijing time, both V4 models move to peak and off-peak rates - peak being 09:00-12:00 and 14:00-18:00 Beijing time, off-peak everything else at half the peak price. Output roughly quadruples at peak: V4 Pro goes from ¥6 to ¥27 per million tokens, V4 Flash from ¥2 to ¥9, both a 350% rise. Input triples. The eye-watering line is cached input on V4 Pro, from ¥0.025 to ¥0.30 - twelve times over, and the source of the “up to 1,100%” headlines. DeepSeek published the new card in yuan, so treat dollar equivalents as approximate until its USD sheet updates.

Here’s the part worth remembering, because it’s the practical case for open weights in a single fact: none of that touches the Fireworks price. A host that runs the weights sets its rates from its own compute costs, not from the model-maker’s business decisions. So from August 17, renting V4 Flash from Fireworks at a flat $0.14 / $0.28 becomes cheaper than renting it from DeepSeek itself, at any hour of the day. For V4 Pro the answer gets genuinely mixed - DeepSeek direct stays cheaper off-peak, while at peak Fireworks wins on output and loses on input - which is its own kind of argument for not wiring your stack to one supplier.

For most knowledge workers, the appeal is unchanged: most of the quality, a fraction of the cost, and no vendor lock-in. For privacy-conscious teams, the bigger appeal is that you can keep it entirely in-house - and self-hosting, like a third-party host, is exactly the option a model-maker’s price rise doesn’t reach.

Alibaba - Qwen

Qwen is one of the most prolific families in AI, and a favorite of people who want to own their model. Alibaba publishes a steady stream of open-weight Qwen releases under the permissive Apache 2.0 license - you can download them, fine-tune them, and self-host. They’re especially strong at multilingual work and at the kind of multi-step “do this, then that” automation that’s becoming the norm.

The new flagship is Qwen3.8-Max, released August 3, 2026 and the strongest model Alibaba has shipped. It’s natively multimodal - images and video are first-class inputs, not bolt-ons - reads a million tokens at once, and ranks second on the public Vision Arena, behind only Gemini. On general intelligence it lands about 8% off the leader, in the same bracket as the frontier flagships.

The reason to care is the price: $2 / $6 per million tokens, roughly a quarter of what Claude Opus 5 or GPT-5.6 Sol charge on output. If your work is document- and image-heavy and Gemini’s bill has been stinging, this is the first credible alternative. It’s API-only as we publish, with the weights promised for the week of August 10 - which would make Alibaba the only lab shipping a frontier-class multimodal model you can also run yourself.

Moonshot AI - Kimi

Kimi, from Beijing-based Moonshot AI, has carved out a clear identity: models built for agentic software engineering - long, multi-file coding jobs where the model plans, writes, tests, and fixes over many steps. Kimi K3, released July 16, 2026, is the family’s leap into the frontier: it scores within about 5% of the very top of the public intelligence rankings - ahead of Claude Opus 4.8 - with coding as its strongest suit and multi-hour autonomous runs across a million-token context. It also debuted at #1 on LMArena’s front-end coding leaderboard, a blind head-to-head where people vote on which model’s UI code they prefer, jumping seventeen places from its predecessor and passing Claude Fable 5. Two honest notes. At $3 / $15 it’s priced like a mid-tier closed model, not a budget one - the era of dirt-cheap Chinese frontier models may be ending. And the weights did land on schedule, on July 27, 2026, which makes K3 the highest-scoring model anyone can run themselves today - though under Moonshot’s own license rather than MIT or Apache. Self-hosting and fine-tuning are fine; if you plan to resell access at scale, read the terms first.

SpaceXAI - Grok

Grok, from SpaceXAI - the renamed xAI, after its merger with Elon Musk’s SpaceX was formalized this month - used to be a specialist you picked for one trick: it’s wired into X (formerly Twitter) and the live web, so it can answer questions about what’s happening right now - breaking news, a trending topic, this morning’s chatter. That’s still a differentiator no one else has - but it’s no longer the main reason to pick it.

Grok 4.6, released August 12, 2026, is the biggest move on this page. It scores 61 on the public intelligence index - about 3% off the lead, tied with GPT-5.6 Sol for third place overall, behind only Claude Opus 5 and Claude Fable 5 - while charging $2 / $6 per million tokens against Sol’s $5 / $30. Same composite score, a fifth of the output price. It also takes first place on GDPval, a test built around realistic professional deliverables rather than puzzles, ahead of both Fable 5 and Sol.

The interesting part is how it got there. Grok 4.6 isn’t a bigger model - it runs on the same 1.5-trillion-parameter foundation as Grok 4.5, and every gain comes from more training after the fact: regenerated worked examples, plus reinforcement learning in environments where the model has to actually finish agent-style jobs. The result shows up as stamina rather than raw cleverness. It stays with a long task across many steps and checks its own work along the way, which is precisely the failure mode that makes agents expensive. A new “xhigh” setting joins low, medium, and high when you want it to think longer.

Two honest caveats. It’s noticeably weaker at shell-driven work - 26% on Terminal-Bench against roughly 34% for both GPT-5.6 Sol and Claude Fable 5, and 65.9% on the DeepSWE coding benchmark against Sol’s 73%. If your agents live in a terminal, that gap is real. And the rival numbers come from SpaceXAI’s own launch table, which quotes competitors’ best published results rather than re-running everything in one harness - fine for direction, not for calling a photo finish.

Practical notes: a 500K context window (half what most of this table offers), text and images in, text out, and a knowledge cutoff of February 1, 2026, so lean on its live search for anything recent. The rate doubles to $4 / $12 on prompts over 200K tokens. There are no downloadable weights. And the pace isn’t letting up - a larger, 2.1-trillion-parameter Grok 4.7 is expected within weeks, with Grok 5 targeted before the end of the year.

Zhipu - GLM

GLM, from Beijing-based Zhipu (which brands its apps as Z.ai), is the dark-horse story of 2026. GLM-5.2 is open-weight under a permissive MIT license, yet it beats GPT-5.5 - OpenAI’s previous flagship - on several real-world coding benchmarks at a fraction of the cost. With a million-token context and strong agentic, tool-using skills, it’s the go-to for teams that want frontier-class coding without frontier bills, or that need to keep everything in-house.

Kimi K3’s weights landing in late July knocked GLM-5.2 off the top of the downloadable rankings on raw score - but not off the top of the list that matters to a lot of teams. GLM-5.2 remains the highest-scoring open model under a genuinely permissive license: MIT, no revenue thresholds, no attribution clause, no separate agreement to sign. If your legal review is the bottleneck rather than your benchmark, this is still the one.

Also worth knowing

• Inkling (Thinking Machines Lab) - the buzziest newcomer: a July 2026 open-weight release under Apache 2.0 from the lab founded by OpenAI’s former CTO, and instantly the strongest US-made open model on the public index. Refreshingly, the lab says so itself: Inkling “is not the strongest overall model available today, open or closed.” One to watch.
• Nemotron 3 Ultra (NVIDIA) - the most completely open release on this page: NVIDIA published not just the weights but the training data, the recipe, and the reinforcement-learning environment, under a license that permits broad commercial use. It scores below the leading Chinese open models, but nothing else at this size is this transparent.
• Llama (Meta) - the family that kicked off the open-weight movement and made local AI mainstream, now in maintenance mode as Meta’s frontier work moves to the closed Muse line. Llama 4 remains downloadable and widely supported, but it’s no longer where the action is.
• MiniMax (M3) - a quietly strong open-weight all-rounder from Shanghai; it matches DeepSeek on the public intelligence index at rock-bottom API prices.
• Mistral (France) - Europe’s flagship lab, focused on small, efficient models that are cheap to run and friendly to data-residency rules. A sensible pick for EU teams and on-device use.

Open vs closed models - what actually matters for you

You’ll see models split into “closed” (you rent access through an API - GPT, Claude, Gemini, Meta’s Muse) and “open” (the weights are published, so you can download and run them - DeepSeek, Qwen, GLM, Llama). The camps even flipped in 2026: Meta, open-weight AI’s original champion, went closed with Muse, while the strongest open models now come from DeepSeek, Zhipu, Moonshot, and a new wave of labs. For a knowledge worker, the difference comes down to three practical questions:

• Where does your data go? With a closed model, your prompts travel to the provider. For most everyday work that’s fine. For sensitive data - patient records, unreleased financials, legal matters - an open model you host yourself keeps everything in your own walls.
• What does volume cost? Closed frontier models are billed per use and add up fast at scale. Open models can be dramatically cheaper, especially if you run a lot of routine work through them.
• Are you locked in? Build everything around one provider’s model and you’re exposed to its price changes and deprecations. Open models - and a model-neutral setup - keep your options open. DeepSeek’s August 17 price rise is the cleanest illustration of the year: because the weights are published, anyone paying too much can move to another host or their own hardware and the increase simply doesn’t arrive.

The honest answer for 2026 is that you’ll probably want both: closed frontier models for the hard 10%, open models for the high-volume 90%. Which leads to the punchline.

You don’t actually have to pick one

Here’s the thing the launch-day hype skips: the best model for any given task is rarely the best model for the next task. Drafting a quick email and untangling a quarter of messy financial data are different jobs, and paying frontier prices for both is like taking a sports car to do the grocery run.

This matters even more once you put AI to work as an agent - software that plans, uses tools, and grinds through a task over many steps on your behalf. Agents burn through far more text than a quick chat does, because they read, act, check the result, and try again, over and over. Run all of that on a premium model and the bill climbs quickly. Run the routine steps on a cheap open model and save the frontier model for the hard part, and you get the same result for a fraction of the cost.

Agents are also where the Cost column above stops predicting your invoice. Some of the cheapest models here take so many turns to finish a job that most of their headline discount disappears, and one of the pricier ones works out cheaper than the budget option it’s supposed to lose to. Cost per task is the number that shows it.

That “use the right engine for each job” approach is exactly what we built MindsHub around. MindsHub Cowork is a single workspace where you hand a whole task to an open-source AI agent - “pull last quarter’s refunds, explain the biggest movers, and build me a dashboard” - and collect the finished work, not a chat transcript. Under the hood, our Model Router is pre-wired across the frontier providers (Anthropic, OpenAI, Google, xAI, Meta) and leading open models (Kimi, DeepSeek, Qwen). You pick your models from a dropdown - no juggling API keys for six different accounts - and switch whenever you like. Change your mind, and your agent, your history, and its memory all carry over. Nothing to migrate.

That’s the whole idea behind being model-neutral: the model is a setting, not a life sentence. It’s why we keep such a close eye on the rankings, and why we can compare these models honestly - we don’t have a horse in the race. We just want each task running on whatever does it best.

If you want to try it, MindsHub Cowork is free, and there is no subscription on either tier. Bring API keys you already have and use any model on this page, paying your provider directly - or skip keys entirely and run our MindsHub Air model on five million tokens a month, enough to delegate a real pile of work. When you want the whole catalog without holding provider accounts, Pro is pay as you go: top up a balance and it draws down at the published per-model rates. Or browse the use-case gallery to see the kinds of tasks people hand off.

How to choose, in 60 seconds

Still want a single recommendation? Match your main job to a starting point:

• Writing, email, summaries, everyday questions → GPT-5.6 (Terra is the value pick) or Claude Sonnet 5.
• Hard analysis, long documents, anything an agent runs for a long time → Claude Opus 5, GPT-5.6 Sol, or Grok 4.6 for the same composite score at a mid-tier price. Claude Fable 5 if the task justifies the premium.
• Questions about what’s happening right now → Grok 4.6, which searches X and the live web natively.
• Big PDFs, slide decks, images, audio, or video → Gemini 3.1 Pro, or Qwen3.8-Max if the volume makes Gemini’s bill hurt.
• High volume on a budget → DeepSeek V4 Flash is the cheapest thing here at $0.14 / $0.28 through a host like Fireworks, with GPT-5.6 Luna, Gemini 3.6 Flash, and GLM-5.2 close behind.
• Coding and technical work → GPT-5.6 Sol or Claude Opus 5, which share the top of the coding-agent rankings, with Kimi K3, GLM-5.2, and DeepSeek as cost-effective open alternatives.
• Privacy-sensitive or on-premises → an open model you host yourself: Kimi K3 for the highest score, GLM-5.2 if you want MIT-license simplicity, DeepSeek or Qwen for cheap.

Then do the one thing benchmarks can’t do for you: run the same real task through two of them and keep the one whose answer you’d actually send.

Frequently asked questions

What’s the best LLM in 2026? There’s no single winner - and the top is closer than ever. As of August 13, 2026, Anthropic’s Claude Opus 5 holds first place on the public intelligence rankings, one point ahead of Claude Fable 5, with OpenAI’s GPT-5.6 Sol and SpaceXAI’s brand-new Grok 4.6 tied about three percent back and Kimi K3 next. But “best” depends on the task: Gemini 3.1 Pro wins on huge documents and video, Grok 4.6 gives you a frontier score at a mid-tier price, and open models like GLM-5.2 and DeepSeek V4 Flash win on cost.

What is Claude Opus 5? Anthropic’s newest model, released July 24, 2026, and the current leader of the public intelligence rankings. It’s aimed at agentic coding and long-running work, reports 96% on the SWE-bench Verified bug-fixing benchmark, shares first place on the public coding-agent rankings with GPT-5.6 Sol, and costs $5 / $25 per million tokens - half the price of Claude Fable 5, which Anthropic’s own documentation still describes as its most capable model. It’s now the default in Anthropic’s coding tools; Claude Sonnet 5 remains the default in the Claude apps for most subscribers.

What is Qwen3.8-Max? Alibaba’s newest flagship, released August 3, 2026. It’s natively multimodal with a million-token context window, ranks second on the public Vision Arena, and scores about 8% off the overall leader - all at $2 / $6 per million tokens, which makes it the best-value pick for image- and document-heavy work. It’s API-only until the open weights land, which Alibaba has promised for the week of August 10, 2026.

What is Grok 4.6? SpaceXAI’s newest model, released August 12, 2026. It scores 61 on the public intelligence index - tied with GPT-5.6 Sol for third place, behind Claude Opus 5 and Claude Fable 5 - and takes first place on GDPval, which scores realistic professional deliverables. It’s a post-training upgrade to Grok 4.5 rather than a bigger model, tuned for agents that run a long time, and it costs $2 / $6 per million tokens, a fifth of GPT-5.6 Sol’s output price. The trade-offs: a 500K context window rather than a million, weaker shell-driven coding (26% on Terminal-Bench against roughly 34% for Sol and Fable 5), and no downloadable weights.

What’s the best free or open-source model? Kimi K3, whose 2.8-trillion-parameter weights landed on Hugging Face on July 27, 2026, is the highest-scoring model you can run yourself - though under Moonshot’s own license, which requires a separate agreement if you resell access to it above $20 million in revenue. If you want a permissive license with no strings, GLM-5.2 is the best MIT-licensed option, with DeepSeek V4 Flash the budget favorite - and DeepSeek’s August 17 API price rise is a neat demonstration of why published weights matter: you can move to another host, or your own hardware, and the increase never reaches you. Qwen3.8-Max joins them when Alibaba ships its weights, due the week of August 10, 2026. All of them rival closed models at a fraction of the cost.

Which LLM is the cheapest? DeepSeek V4 Flash, at $0.14 / $0.28 per million tokens through a host like Fireworks - but the question now has a second half: from whom. DeepSeek’s own API raises V4 rates between roughly 2.25 and 4.5 times on August 17, 2026, with peak and off-peak windows, while third-party hosts running the open weights set their own prices and aren’t moving. Renting the same model from a host becomes the cheaper door from that date. Behind Flash, GPT-5.6 Luna is remarkably close at $0.20 / $1.20 after OpenAI cut it 80% on July 30, 2026, and self-hosting is cheapest of all at scale, since nobody’s price rise reaches your own hardware. For routine work, any of these - plus GLM-5.2, Gemini 3.6 Flash, or Meta’s Muse Spark - costs a small fraction of the frontier flagships while handling the job well.

What is Claude Fable 5, and why was it unavailable? Fable 5 is Anthropic’s premium top-tier model, and by its own documentation still the company’s most capable - though Claude Opus 5 now edges it on the composite rankings at half the price. Days after launching in June 2026, a US export-control order forced Anthropic to switch it off worldwide over its ability to find software vulnerabilities. With new safeguards in place, the order was lifted and Fable 5 returned globally on July 1, 2026, priced at $10 / $50 per million tokens.

Is GPT better than Claude? It’s a photo finish that changes with each release. As of August 13, 2026, Claude leads: Claude Opus 5 sits at the top of the public intelligence rankings with GPT-5.6 Sol about three percent behind - now sharing that spot with SpaceXAI’s Grok 4.6 - while Sol and Opus 5 share first place on the coding-agent rankings, at the same input price. At the everyday tier - GPT-5.6 Terra vs Claude Sonnet 5 - they’re close enough that price and style should decide. Try both on a real task and keep whichever holds up.

What’s the best LLM for coding? GPT-5.6 Sol and Claude Opus 5 share the top of the public coding-agent rankings, with the rest of the Claude family right behind. Open models have closed the gap fast - Kimi K3, GLM-5.2, and DeepSeek all post frontier-class coding results at lower cost, which is why they’re popular for high-volume development.

Do I have to commit to one model? No - and you probably shouldn’t. Tools like the MindsHub Model Router let you route each kind of work to the model that suits it: cheap open models for routine steps, frontier models for the hard parts. You keep your work when you switch.

How often does this change? Constantly. Major new models ship every few weeks, and the rankings reshuffle even faster. That’s the real argument for staying flexible rather than betting everything on one model - bookmark the Artificial Analysis leaderboard and revisit it now and then.

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MindsHub by MindsDB is the unified workspace where open-source models get things done for you. Delegate entire projects through MindsHub Cowork and collect finished, shareable results - work runs on interchangeable open-source agent harnesses, Anton and Hermes. The Model Router spans commercial and open models, so you can match every job to the right engine and switch anytime. Founded 2018 in Berkeley. Backed by Benchmark, Mayfield, Y Combinator, and NVIDIA.